Automatic patient-ventilator asynchrony detection framework using objective asynchrony definitions

Patient-ventilator asynchrony is one of the largest challenges in mechanical ventilation and is associated with prolonged ICU stay and increased mortality. The aim of this paper is to automatically detect and classify the different types of patient-ventilator asynchronies during a patient's breath using the typically available data on commercially available ventilators. This is achieved by a detection and classification framework using an objective definition of asynchrony and a supervised learning approach. The achieved detection performance of the near-real time framework on a clinical dataset is a significant improvement over current clinical practice, therewith and, this framework has the potential to significantly improve the patient comfort and treatment outcomes.